Update README.md

README.md

@@ -1,199 +1,110 @@
 ---
-library_name: transformers
-tags: []
+language: ko
+pipeline_tag: text-generation
+license: llama3.1
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-[More Information Needed]
-
-### Out-of-Scope Use
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-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-[More Information Needed]
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-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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-### Training Procedure
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-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
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-[More Information Needed]
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-
-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
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-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
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-[More Information Needed]
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-### Results
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-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
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-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
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-### Compute Infrastructure
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-#### Hardware
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-#### Software
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-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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-**BibTeX:**
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-**APA:**
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-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## More Information [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
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+### 1. Model Description
+- KONI (KISTI Open Natural Intelligence) is a specialized large language model (LLM) developed by the Korea Institute of Science and Technology Information (KISTI). This model is specifically designed for science and technology, making it highly effective for tasks in these fields.
+
+### 2. Key Features
+- **Specialized in Science and Technology:** The model is explicitly trained on a vast and specialized corpus of scientific and technological data.
+- **Enhanced Performance:** This version of KONI shows significantly improved performance compared to its initial release in December, 2023.
+- **Base Model:** The base model for KONI-Llama3.1-70B-Instruct is Meta-Llama-3.1-70B-Instruct.
+- **Alignment:** SFT (Supervised Fine-Tuning) and DPO (Direct Preference Optimization) are applied
+
+### 3. Data
+- Approximately 11k SFT data and 7k DPO data are used.
+- **SFT Data:** The SFT data includes both internally generated data and publicly available data on Hugging Face, translated into Korean where necessary.
+- **DPO Data:** The DPO data consists of translated and curated data from argilla/dpo-mix-7k.
+
+### 4. Benchmark Results
+The performances were evaluated using the [LogicKor](https://github.com/instructkr/LogicKor) benchmark dataset as follows:
+Will be updated soon
+<!--
+| Metric         | Score |
+|:--------------:|:-----:|
+| Reasoning      |  8.57 |
+| Math           |  8.93 |
+| Writing        |  9.43 |
+| Coding  |  8.93 |
+| Comprehension        |  8.93 |
+| Grammar      |  8.64 |
+| Single-turn      |  8.81 |
+| Multi-turn    |  9.00 |
+| **Overall**    | **8.91** |
+-->
+### 5. How to use the model
+```python
+import transformers
+import torch
+
+model_id = "KISTI-KONI/KONI-Llama3.1-70B-Instruct-preview"
+
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model_id,
+    model_kwargs={"torch_dtype": torch.bfloat16},
+    device_map="auto",
+)
+
+pipeline.model.eval()
+
+instruction = "안녕? 너는 누구야?"
+
+messages = [
+   {"role": "user", "content": f"{instruction}"}
+    ]
+
+prompt = pipeline.tokenizer.apply_chat_template(
+        messages, 
+        tokenize=False, 
+        add_generation_prompt=True
+)
+
+terminators = [
+    pipeline.tokenizer.eos_token_id,
+    pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
+]
+
+outputs = pipeline(
+    prompt,
+    max_new_tokens=2048,
+    eos_token_id=terminators,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.9
+)
+
+print(outputs[0]["generated_text"][len(prompt):])
+```
+```
+안녕하세요! 저는 KISTI의 KONI입니다. 과학기술 데이터를 전문으로 처리하며, 여러분의 연구와 질문에 최선을 다해 도움을 드리겠습니다. 무엇을 도와드릴까요?
+```
+
+### 6. Citation
+**Language Model**
+```text
+@article{KISTI-KONI/KONI-Llama3.1-70B-Instruct-preview,
+  title={KISTI-KONI/KONI-Llama3.1-70B-Instruct-preview},
+  author={KISTI},
+  year={2024},
+  url={https://huggingface.co/KISTI-KONI/KONI-Llama3.1-70B-Instruct-preview}
+}
+```
+  
+### 7. Contributors
+- KISTI, Large-scale AI Research Group
+
+### 8. Special Thanks
+- [@beomi](https://huggingface.co/beomi)
+- [@kuotient](https://huggingface.co/kuotient)
+- KyungTae Lim
+
+### 8. Acknowledgement
+- This research was supported by Korea Institute of Science and Technology Information(KISTI).
+- This work was supported by the National Supercomputing Center with supercomputing resources including technical support (KISTI).
+
+### 9. References
+- https://huggingface.co/meta-llama/Meta-Llama-3.1-70B
+- https://huggingface.co/meta-llama/meta-llama/Meta-Llama-3.1-70B-Instruct